South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia.
South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW, Sydney, New South Wales, Australia; Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia; South Western Sydney Cancer Services, NSW Health, Sydney, New South Wales, Australia.
Clin Oncol (R Coll Radiol). 2024 Jul;36(7):e197-e208. doi: 10.1016/j.clon.2024.03.008. Epub 2024 Mar 16.
The objective of this study was to develop a two-year overall survival model for inoperable stage I-III non-small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy.
A federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011-2019. A logistic regression model was trained for the 2011-2016 curative patient cohort and validated for the 2017-2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan-Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups.
1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01).
Federated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care.
本研究旨在通过联邦（分布式）学习网络，利用常规放射肿瘤学数据，为无法手术的 I-III 期非小细胞肺癌（NSCLC）患者建立一个为期两年的总生存率模型，并评估其对根治性与姑息性放疗的决策支持的潜力。
在七个诊所中安装了一个联邦基础设施，用于提取、去识别、标准化、图像分析和建模，以获取 2011-2019 年接受治疗的患者的临床和影像学特征以及生存信息。在 2011-2016 年的根治性患者队列中训练了一个逻辑回归模型，并对 2017-2019 年的队列进行了验证。使用单变量和基于模型的分析选择特征，并使用引导法进行优化。通过接受者操作特征（ROC）和相应的曲线下面积（AUC）、C 指数、校准指标和 Kaplan-Meier 生存曲线来评估系统性能，通过模型概率四分位数来定义风险组。使用治疗组之间的倾向匹配进行病例对照分析来评估决策支持。
共纳入 1655 例患者数据集。总体模型 AUC 为 0.68。根据模型，58%接受姑息性放疗的患者预测风险为低-中度，与模型分类为高风险的接受根治性放疗的患者的生存时间无显著差异（p=0.87 和 0.061）。当根据风险组和模型指示的治疗进行生存模拟时，预计两年时的生存率将提高 11%（p<0.01）。
通过多个机构的数据进行联邦学习，可以开发和验证肺癌的决策支持系统，并量化其在实践中的潜在影响。这为个性化医疗铺平了道路，在个性化医疗中，可以更紧密地根据常规护理中的个体患者详细信息做出决策。
